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Human Body-Related Disease Diagnosis Systems Using CMOS Image Sensors: A Systematic Review

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Human Body-Related Disease Diagnosis Systems Using CMOS Image Sensors: A Systematic Review

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According to the Center for Disease Control and Prevention (CDC), the average human life expectancy is 78.8 years. Specifically, 3.2 million deaths are reported yearly due to heart disease, cancer, Alzheimer’s disease, diabetes, and COVID-19. Diagnosing the disease is mandatory in the current way of living to avoid unfortunate deaths and maintain average life expectancy. CMOS image sensor (CIS) became a prominent technology in assisting the monitoring and clinical diagnosis devices to treat diseases in the medical domain. To address the significance of CMOS image ‘sensors’ usage in disease diagnosis systems, this paper focuses on the CIS incorporated disease diagnosis systems related to vital organs of the human body like the heart, lungs, brain, eyes, intestines, bones, skin, blood, and bacteria cells causing diseases. This literature survey’s main objective is to evaluate the ‘systems’ capabilities and highlight the most potent ones with advantages, disadvantages, and accuracy, that are used in disease diagnosis. This systematic review used PRISMA workflow for study selection methodology, and the parameter-based evaluation is performed on disease diagnosis systems related to the human body’s organs. The corresponding CIS models used in systems are mapped organ-wise, and the data collected over the last decade are tabulated.
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sensors
Review
Human Body-Related Disease Diagnosis Systems Using CMOS
Image Sensors: A Systematic Review
Suparshya Babu Sukhavasi 1, Susrutha Babu Sukhavasi 1, Khaled Elleithy 1, * , Shakour Abuzneid 1
and Abdelrahman Elleithy 2


Citation: Sukhavasi, S.B.; Sukhavasi,
S.B.; Elleithy, K.; Abuzneid, S.;
Elleithy, A. Human Body-Related
Disease Diagnosis Systems Using
CMOS Image Sensors: A Systematic
Review. Sensors 2021,21, 2098.
https://doi.org/10.3390/s21062098
Academic Editor: Roni El-Bahar
Received: 1 February 2021
Accepted: 11 March 2021
Published: 17 March 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
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conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA;
susukhav@my.bridgeport.edu (S.B.S.); ssukhava@my.bridgeport.edu (S.B.S.); abuzneid@bridgeport.edu (S.A.)
2
Department of Computer Science, William Paterson University, Wayne, NJ 07470, USA; elleithya@wpunj.edu
*Correspondence: elleithy@bridgeport.edu; Tel.: +1-203-576-4703
Abstract:
According to the Center for Disease Control and Prevention (CDC), the average human
life expectancy is 78.8 years. Specifically, 3.2 million deaths are reported yearly due to heart disease,
cancer, Alzheimer’s disease, diabetes, and COVID-19. Diagnosing the disease is mandatory in the
current way of living to avoid unfortunate deaths and maintain average life expectancy. CMOS
image sensor (CIS) became a prominent technology in assisting the monitoring and clinical diagnosis
devices to treat diseases in the medical domain. To address the significance of CMOS image ‘sensors’
usage in disease diagnosis systems, this paper focuses on the CIS incorporated disease diagnosis
systems related to vital organs of the human body like the heart, lungs, brain, eyes, intestines, bones,
skin, blood, and bacteria cells causing diseases. This literature survey’s main objective is to evaluate
the ‘systems’ capabilities and highlight the most potent ones with advantages, disadvantages, and
accuracy, that are used in disease diagnosis. This systematic review used PRISMA workflow for
study selection methodology, and the parameter-based evaluation is performed on disease diagnosis
systems related to the human body’s organs. The corresponding CIS models used in systems are
mapped organ-wise, and the data collected over the last decade are tabulated.
Keywords:
CMOS; CMOS image sensors; medical imaging systems; medical applications; biomedical
CMOS image sensors; implantable CMOS image sensors; smartphone CMOS image sensors
1. Introduction
The brain, heart, eyes, intestines, and lungs are the most affected organs in the current
living way. The monitoring of human organs like the brain, eyes, heart, intestine, lungs,
and vital signs (shown in Figure 1) observation is mandatory for secure and healthy
human living to avoid early deaths. According to the year 2019 statistics [
1
], the death
rate caused by heart failure is 647,000 every year, and death is recording for every 37 s as
per CDC because of cardiovascular disease. The heart’s major diseases are cardiac arrest,
coronary artery disease (CAD), congestive heart failure, aortic disease, peripheral arterial
disease, etc. According to a global burden disease study [
2
] conducted on neurological
disorders from 1990 to 2017, around 100 million Americans are being affected by one
or more neurological disorders. Among them, Alzheimer’s, Parkinson’s, migraine, and
stroke are the foremost neurological disorders leading to death. The average death rate of
Alzheimer’s disease is 258,600 deaths, Parkinson’s disease is 30,000 deaths, and stroke is
172,000 deaths. According to Leukemia & Lymphoma Society [
3
], a death is recording due
to blood cancer for every nine minutes, six deaths are recording for every hour, 156 deaths
are recording for every day, and the estimated death rate in the United States would be
56,840 by the end of 2020 due to leukemia, lymphoma, and myeloma which are the major
blood disorders. In addition, diabetes [
4
] is becoming the seventh major cause of death in
the United States by recording 270,702 deaths in 2017. By the year 2040 [
5
], an estimation
of 78.4 million people over the age introduction of 18 years or older will affect by arthritis,
Sensors 2021,21, 2098. https://doi.org/10.3390/s21062098 https://www.mdpi.com/journal/sensors
Sensors 2021,21, 2098 2 of 38
of which two-thirds of them would be women. For every five Americans, one person is
developing skin cancers during their lifetime, leading to an estimation of 9500 every day [
6
].
The major skin cancers are melanoma, non-melanoma skin cancer, basal cell carcinoma,
and squamous cell carcinoma, affecting 3 million people every year. By the year 2020,
6850 deaths are recorded due to melanoma skin cancer. By 2050 [
7
], it is estimated that
8.96 million people will become either visually impaired or blind as per CDC. The national
institute of occupational safety and health report says 2000 USA workers are prone to eye
accidents every day. According to CDC [
8
], 14.8 million adults are diagnosed with cancers,
in which 266,000 people are diagnosed with gastro intestine (GI) cancers, and 14,400 deaths
are recorded due to GI cancers in 2014.
Figure 1. Vital signs and organs of the human body focused in our literature survey [9].
In our survey, we concentrate on literature related to the CMOS image sensors assisting
medical devices in diagnosing diseases affecting vital signs and parts of the human body.
Our contributions are listed below:
We have conducted a novel systematic review on CIS utilization in disease diagnosis
in the medical field.
We have extracted data and evaluated by specifying the vital parameters required for
medical systems performing disease diagnosis shown in Table 2.
Based on our literature survey, we have tabulated all the available technical specifica-
tions related to CMOS image sensors in Appendix ATable A1.
The remainder of the paper is organized as follows. We used PRISMA workflow
for study selection methodology in Section 2. In Section 3, we discussed CIS’s role in
diagnosing the diseases affecting the human body and the data extraction with evaluation
in Section 4. Organ wise and year wise utilization of CIS models in disease diagnosis
systems are discussed in Section 5. In Section 6, conclusions are offered.
2. Study Selection Methodology
We reviewed the literature covering the years between 2009 and 2021 that is drawn
from the article search engines like “Google Scholar”, “IEEE Xplore”, “SpringerLink”,
“MDPI”, “Pub Med”, “ELSEVIER”, “ARXIV”, “Scopus”, “Science citation index (SCI)”,
“SPIE Digital Library”, “ACM Digital Library”, “ASME.” The review was conducted
systematically by a PRISMA workflow, as shown in Figure 2. Different tools were available
for searching and selection of publications like “DOAJ,” “EndNote,” and “Microsoft Excel.”
Sensors 2021,21, 2098 3 of 38
We included the publications involving CMOS image sensors used in medical applications
to monitor different body parts like the brain, bones, eyes, blood, intestine, heart, and lungs.
We classified the searched publications into qualitative, quantitative, book, and editorial
categories. The entire search was conducted based upon the words used in the “keyword”
section. We searched and collected articles of about 190 papers, and finally, we bound
the selection to 42 full-text articles from the year 2009 to the year 2021. All the 42 papers
are cited in the reference section, and “EndNote” software was used for managing the
references. As our literature survey is conducted on CMOS image sensors in the medical
field, we primarily focused on the articles from “Elsevier” and “Google Scholar.” We
excluded the articles that are not in English, particularly those irrelevant since the imaging
systems used were not sensing anything. Some articles are also excluded because they
did not mention the usage of CIS in their field experiments. The other articles excluded
are duplicate articles, unreachable articles. The entire flowchart for the selection process,
including identification, screening, eligibility, and inclusion, is shown in Figure 2[
10
].
Based on the study selection methodology using PRISMA workflow, we have classified our
literature survey according to vital signs and organs of the human body-related disease
diagnosis systems embedded with CMOS image sensors, as shown in Figure 3.
Figure 2. PRISMA workflow for the process of article selection. Figure adapted with permission from [10].
Sensors 2021,21, 2098 4 of 38
Figure 3.
Classification of complementary metal-oxide semiconductor image sensor (CIS) embedded disease diagnosis
systems relating to vital signs and organs of the human body.
3. Role of CIS in Human Body-Related Disease Diagnosis Systems
An image sensor is a sensor that receives the incident photons from light and converts
them into electrons. Two types of image sensors are introduced into the market; charge-
coupled device (CCD) and CMOS image sensors based on complementary metal-oxide
semiconductor (CMOS) technology. Due to the expensive manufacturing process and
specific fabrication, and high-power consumption of CCD’s, CMOS image sensors are
preferred in most of the fields in current-day technology. There are two types of pixel
structures present in CMOS image sensors, namely, passive pixel sensors and active pixel
sensors. Passive pixel sensors are introduced earlier with no amplification inside the
circuitry, and because of column capacitance, high noise and low sensitivity is occurring in
passive pixel sensors. However, active pixel sensors lead over the passive pixel sensors
due to its advantages of amplifier incorporated into the pixel. Increased pixel performance
and power consumption are also very less compared to CCD’s. CMOS image sensors are
playing a prominent role in medical sciences in such a way that they are implemented
in every part of the body. Around 15 million deaths were caused by ischemic heart
disease and stroke globally, and finding the methods to identify them nowadays is crucial.
Diagnostic modules like X-ray, magnetic resonance imaging (MRI), computer tomography,
and echocardiography are used to rescue many lives. Still, these modules can produce black
and white images only. However, the colored images can provide a lot of information to
the physicians to detect and apply suitable treatment. The improvements in CIS technology
bring more quality color images. Its miniature size can incorporate anywhere into the
human body for diagnosis and provide the patient information accurately to the physicians
for better treatments to save lives.
CIS’s involvement in biomedical applications is rapidly increasing day by day, and
technical advancements are being made to meet the design specifications of present-day
human needs, due to CIS’s compatibility in terms of its characteristics that are high dynamic
range, less power consumption, low manufacturing cost, and on-chip functionality. These
Sensors 2021,21, 2098 5 of 38
characteristics made CIS the preferred imaging component in most biomedical applications
to perform all its functions alone or with less power.
Because of the high costs in the health care system, the time duration in hospital
stays is getting shorter day by day, and patients are returning home with sickness and
need a continuous health monitoring process. The patients with chronic diseases should
be monitored continuously, especially the old aged population, increasing every year,
preferring to live alone and do not want to be in an assisted living facility. Sophisticated
technology makes the monitoring devices be minimized with built-in battery or portable
power sources leading to a novel, innovative world of possibilities. For instance, cell
phones that are portable and battery-operated can do real-time monitoring and control
a lot of applications. Implantable and wearable technologies sense the parameters of
different diseases. They will transfer the patient’s data to the treating facility or directly
to the patient to take the corresponding action. For example, if the blood glucose levels
cross the limits, this technology alerts the patient to take the required insulin amount.
Before any implantable application be introduced into human society, sufficient trials will
be conducted on rats, so here, we focused our survey in rat-based experiments to know
the advancements in implantable applications that are not introduced into the market.
Smartphones are becoming dependable devices in daily human living, especially in using
smart home appliances, cars, and navigation. These smartphones are being used in medical
fields as detectors, analyzers, and conducting diagnostic tests.
3.1. Disease Diagnosis Systems Related to Blood
Quantifying hemoglobin concentration is an essential process in most clinical lab-
oratories to analyze the postoperative bleeding, status of autologous blood transfusion,
anemia, etc. Over 400 million hemoglobin tests for concentration are conducted every
year to identify blood-related diseases and disorders in the United States. Dong-Sik Kim
et al. [
11
] developed a new technique to make hemoglobin concentration measurement
cost effective and straightforwardly. It provides accurate hemoglobin values from samples
of blood without involving any reagents harmful to the environment and works with the
help of LED and CIS as shown in Figure 4.
Figure 4.
Experimental setup for hemoglobin concentration measurement using CIS. Figure adapted
with permission from [11].
Mini chemical sensors play a crucial role in medical applications like patient bed
monitoring and disposal sample answer systems, personal safety, and implantable chips.
Daisy S. Daivasagaya et al. [
12
] focused on luminescence sensors microarrays, which
provide more advantages like a fast response, no additional reagents required, and will
not spoil the sample media. He described a portable optical gaseous oxygen sensor
microsystem using xerogel sensor elements, contact printed on the top of a trapezoidal
lens like microstructures molded into polydimethylsiloxane (PDMS). PDMS is a soft,
biocompatible, flexible, and an optically transparent silicon-organic polymer suitable to
fabricate the lens, diffusers, and filters of the optical sensors. The imaging sensor system’s
block diagram is shown in Figure 5.
Sensors 2021,21, 2098 6 of 38
Figure 5.
(
a
) Imaging sensor block diagram. (
b
) Ultra-thin packaged CIS with a Canadian one-cent
coin as a reference. (
c
) Cross-sectional view. (
d
) A packed imager IC. Figure adapted with permission
from [12].
Generally, in old-aged people, a complete blood count (CBC) is one of the resources
for blood tests to find the health status of patients with heart diseases. Most of the CBC
instruments like hematology analyzers and hemocytometers are implemented traditionally.
These instruments need heavy equipment like some actuators, countertop, and liquid
systems. So, these instruments are limited to laboratories in hospitals and research centers.
An alternative solution to calculate CBC is using Raman scattering and optical microscopy.
Xu Liu et al. [
13
] developed a super-resolution microfluidic cytometer to perform CBC
tests. The microfluidic cytometer prototype consists of a PDMS microfluidic channel with
attached CIS, PCB (printed circuit board) incorporated in FPGA board, and a MATLAB
GUI working laptop shown in the paper. Its black-box approach is shown in Figure 6. This
lens-free microfluidic cytometer is well suited for high accuracy whole blood recognition
and its count in the point-of-care diagnosis of old-aged people.
Figure 6. Microfluidic cytometer for a complete blood count.
The term agglutination is an antigen and antibody reaction, which will happen during
the clumping of visible particles formation. In this process, antibody combines with the
respective antigen with the electrolytes at a specific pH and temperature. Chung Hsiang
Lu et al. [
14
] developed a finger-powered agglutination lab chip with a CMOS image sensor
outside that was affordable and cost-effective. For demonstration, blood grouping is used
to verify the function of the finger power agglutination lab chip. The antibodies of blood
are loaded before into the antibody reaction chamber in the lab chip. Then, the sample
of blood will be pushed into the antibody reaction chamber by a finger-pressed power
actuation to start the hemagglutination reaction to find the type of blood in the detection
area of the on-chip CIS mini sensing system. Without external power, finger-based power
actuation is well suitable for low-cost applications. This system will separate the red blood
Sensors 2021,21, 2098 7 of 38
samples from the whole blood by pressing finger powered pump. The comprehensive
blood test can be completed by pressing the finger 5–6 times on the agglutination-chip, as
shown in Figure 7.
Figure 7.
(
a
) Fingered powered agglutination lab chip. (
b
) Homemade CIS-based mini sensing system
and finger powered agglutination lab chip (
c
) Antibodies were preloaded and pressing procedures,
the agglutination lab chip is placed on CMOS image sensor of the homemade mini system. Figure
adapted with permission from [14].
During the study of brain imaging, experiments will be conducted on small animals
like rats under anesthesia because the brain’s activities will be hard to monitor under
nonanesthetized conditions. Makito Haruta et al. [
15
] developed an ultra-small CMOS
imaging device to monitor brain activities under non-anesthetized conditions. In addition,
the author demonstrated the blood flow velocity detection in the brain using the CIS over a
long period of non-anesthetized conditions. This imaging device is implanted in a rat head
for demonstration shown in the paper cited. Its black-box approach is shown in Figure 8.
Figure 8. Blood flow velocity detection.
The future expectations of this device will contribute to the actual brain functional
mechanisms of animals’ behavior.
The most familiar metabolic disease globally is diabetes, and its main symptom is
the high level of glucose in the blood. It causes the obligations like kidney failure and
loss of sight, so keeping the glucose levels under control every day is crucial for diabetic
patients. To perform this glucose monitoring daily, a blood sample needs to be taken from
the fingertip. Takashi Tokuda et al. [
16
] uses an optical sensing scheme, a unique technique
in which glucose sensors use fluorescent hydrogel, and its black-box approach is shown
in Figure 9.
Sensors 2021,21, 2098 8 of 38
Figure 9. Implantable glucose sensing system.
Jasmine Pramila Devadhasan et al. [
17
] developed a CIS-based immunodiagnosis
system to detect the (HIV) human immunodeficiency virus. The image sensor in the system
counts the photons with respect to antigen concentration of HIV and converts the photon
count into digital numbers. Its black-box approach is shown in Figure 10.
Figure 10. CMOS image sensor-based human immunodeficiency virus (HIV) detection.
Diabetes is the most common disease, which is increasing rapidly across the world.
Regular monitoring the glucose levels is the most suitable method to control diabetes in
old-aged people by not making them prone to peripheral neuropathy. Jasmine Pramila De-
vadhasam et al. [
18
] developed a whole blood glucose analysis system using a smartphone
camera using a point-of-care methodology. Its black-box approach is shown in Figure 11.
Figure 11. Whole glucose blood analysis.
Sensors 2021,21, 2098 9 of 38
Using the enzyme kinetic method, a glucose assay is taken, is reacted with the immo-
bilized assay reagent, and then exposed to light that produces a color captured by CIS. The
corresponding digital value is displayed on the smartphone screen.
Salinity detection is also one of the crucial parameters to monitor for the protection of
ocean management. If the sea salt level is above the normal value, it may cause oxygen
and osmosis concentration changes. Iftak Hussain et al. [
19
] developed a smartphone-
based salinity sensor to monitor the sea environment’s salt level. Two types of methods
are proposed for sensing based on direct transmission, and evanescent field absorption
approaches are shown in Figure 12a,b.
Figure 12.
(
a
) Salinity detection using direct transmission smartphone-based approach.(
b
) Smartphone-
based salinity detection using evanescent field absorption approach. (
c
) Steps involved in measuring
salinity in water. Figure adapted with permission from [19].
The salinity detection and analysis are performed on two different Android platforms.
Using a smartphone, one can communicate and share the data in real-time from the remote
center to the seawater monitoring unit. The author also mentioned that this device is for
measuring the daily salt intake measurement for diabetic patients.
Toxic gases like hydrogen fluoride, ammonia, and chlorine cause harmful effects
on human beings like skin diseases and diarrhea and sometimes lead to death. Jasmine
Pramila Devadhasan et al. [
20
] developed a smartphone-based toxic gas detection, which
is a handheld operated device in real time. In this detection process, toxic gases will be
detected with the help of titanium nanoparticles, which are blended with polyvinyl alcohol
hydrogel strips then mixed with chemically reactive colors. These colors will change with
respect to base acid reactions. The array strips will be monitored by a colorimetry system
and sort them in chrominance data form. These signals will be sent to the smartphone
and display the levels of toxic gases detected on the screen by opening the smartphone
application called “toxic gas detection,” shown in Figure 13. The toxic gas detection also
helps avoid exposure to different toxic gases, causing anemia, a blood-related disorder.
Anemia is a common blood disorder across the world and is also called as Global
Burden of Disease. Particularly an iron-deficiency anemia, which suffers around 1.5 bil-
lion human lives, caused due to the insufficient amount of iron makes your body not
produce enough red blood cells to carry the oxygen (hemoglobin). Hemoglobin deficiency
causes dental disorders, neurological disorders, heart diseases, metabolic changes, and
endocrine disorders [
3
]. Junho Lee et al. [
21
] developed a chemical-free smartphone-
based hemoglobin concentration detection system using Photo Thermal Angular Scattering
technology (PTAS) called in short m-PTAS, as shown in Figure 14.
Sensors 2021,21, 2098 10 of 38
Figure 13.
(
a
) Smartphone-based toxic gas detection system. (
b
) Instrumental setup for toxic gas de-
tection microarray reader. (
c
) Stepwise illustration of smartphone application “Toxic Gas Detection.”
Figure adapted with permission from [20].
Figure 14.
(
a
) Photo Thermal Angular Scattering technology (m-PTAS) sensor. (
b
) Blood sample
capillary tube loaded into m-PTAS module. (
c
,
d
) Smartphone application “meaHb” displays the
computed hemoglobin [21].
This module is portable, chemical-free, and a speedy hemoglobin detection mecha-
nism. Blood samples are taken into a capillary tube and loaded into m-PTAS module for
computing and hemoglobin analysis. A dedicated android application called “meaHb” is
developed to perform the hemoglobin analysis. This module is affordable, disposable, fast,
chemical-free, portable, and self-observable for hemoglobin-related blood disorders like
anemia by taking the input blood samples of less than 150 nL.
Hongying Zhu et al. [
22
] developed a portable and low-cost cytometry imaging
platform incorporated with the cell phone to measure hemoglobin density and white and
red blood cells from human blood samples. These measurements help in clinical tests
Sensors 2021,21, 2098 11 of 38
to analyze the health conditions and diagnose the different blood disorders like anemia
and leukemia. A dedicated android application called “Blood Analysis” is designed to
perform the operations. Two AA batteries are included in a base attachment and a universal
port to hold three components for white blood cell counting, red blood cell counting, and
hemoglobin measurements. Three separate test procedures are conducted individually to
identify the density of hemoglobin and white and red blood cells, as shown in Figure 15.
Its software approach is shown in Figure 16.
Figure 15.
(
A1
,
A2
) Illustration and picture of our cell-phone based blood analysis platform. (
B1
,
B2
)
Setup for white blood cell counting device. (
C1
,
C2
) Setup for red blood cell counting device. (
D1
,
D2
)
Setup for a hemoglobin measurement device. Figure adapted with permission from [22].
Figure 16.
(
A
F
) Steps involved in the Android application to perform blood analysis and display
results on a smartphone. Figure adapted with permission from [22].
Summary
From the surveyed data, the disease diagnosis systems are classified into three cate-
gories used for hemoglobin measurement, glucose measurement and analysis, and whole
blood analysis. Anemia, diabetes, and immunodeficiency are the primary diseases caused
Sensors 2021,21, 2098 12 of 38
due to lack of hemoglobin, high glucose level, and red blood cell deficiency, respectively.
For hemoglobin measurement, we studied three diagnosis systems, namely, hemoglobin
measurement [
11
], blood analysis [
22
], portable chemical-free hemoglobin assay [
21
] with
their functional parametric data available in their articles such as size, error rate and
coefficient of variation, and the blood sample size in which the portable chemical-free
hemoglobin assay [
21
] is efficient system due to its benefits like, requirement of least
blood sample size of less than 1
µ
L, giving result in less than 8 s with an accuracy of 99%,
and having advantages of such low cost, chemical-free procedure, and no requirement of
medical personnel. For glucose measurement, we studied the systems, namely, glucose
sensing [
16
] and whole blood glucose analysis [
18
], in which Glucose sensing [
16
] is a
continuous measuring process by implanting a small steel-covered chip into the human
body. In contrast, whole blood glucose analysis [
18
] is entirely an external application
using a smartphone by taking a blood sample. Among them, complete blood glucose
analysis [
18
] is efficient due to its smart functions, no internal nonspecific interactions,
and also avoidance of implanting procedures that cause infection risk. For whole blood
analysis, we studied the systems, namely, microfluidic cytometer [
13
], finger powered
agglutination lab chip [
14
], blood analysis [
22
] in terms of blood sample size and functional
capabilities in which blood analysis [
22
] is an efficient system as its blood sample size is of
10
µ
L, it provides the result in 10 s with an accuracy of 93%, and it is totally operated with
a smartphone, which can transmit data wirelessly and thus leads to ease in remote sensing.
3.2. Disease Diagnosis Systems Related to Brain
Optogenetics technology is a demanding technology to implement light sensitivity
on the different cell membranes using a genetic framework. This technology generated
different techniques to investigate neural systems like the peripheral nervous system and
brain. T. Tokuda et al. [
23
] developed an integrated microLED array with optical imaging
functionality.
The black-box approach of CIS-based neural interface device is illustrated in Figure 17.
The brain slice is placed on the top of the surface on an integrated neural device, and light is
stimulated from the multifunctional CIS, which is attached under the LED array. Single and
multi-site stimulations are applied and obtained the on-chip image of the hippocampus
slice of a mouse used to observe neural activities. This device is well suitable for on-chip
brain imaging for in vivo and in vitro experiments.
Figure 17. On-chip bio imaging.
To observe the neural activities in the brain, fluorescence measurement is preferred
over the electrical detection method because the fluorescence detection method is able
to measure a large amount of data over a large area. Jun Ohta et al. [
24
] developed an
implantable CMOS imaging device to record the neural activities in a mouse’s brain with
minimal invasiveness. Its black-box approach is shown in Figure 18.
Sensors 2021,21, 2098 13 of 38
Figure 18. Implantable CMOS image sensor for neural activities measurement.
This device can be directly implanted into the brain, and it has a completely dedicated
CIS to excite the fluorescence on the flexible substrate. The head of the device is implanted
into the mouse brain, and the remaining part outside of the brain is connected to the mini-
PCB kept on the back of the mouse. A cable was connected from PCB to the mouse to move.
In order to implant many chips in small animals, it is quite important to deliver the power
and signals without wires because more wires can limit the free emotional behavior of the
mouse. To meet this requirement, Kiyotaka Sasagawa et al. [
25
] developed an approach to
communicate and transmit the power and signals for a short-range. He used the strategy
of conducting properties of living tissues in animals for power communication, and its
black-box approach is shown in Figure 19.
Figure 19.
Wireless intrabrain image transmission using CMOS image sensor implanted in rat head.
To control brain activities optically, optical stimulation techniques have been used,
called Optogenetics. Makito Haruta et al. [
26
] proposed an implantable Optogenetic device
for monitoring brain activities in awake animals with its black-box approach shown in
Figure 20. This device incorporated with CIS can simultaneously perform brain imaging
and optical stimulations and successfully evoke neural activities.
Gian Nicola Angotzi et al. [
27
] developed an implantable CIS probe for simultaneous
large-scale neural recordings named “SiNAPS.” It was proposed to meet the requirements
in neuroscience to analyze complex brain functions. Incorporating this device into pro-
cedures will allow capturing huge distributed cellular processing that characterizes and
executes the brain’s complex functions, which will be useful in neural activity observations.
Its black-box approach is shown in Figure 21.
Sensors 2021,21, 2098 14 of 38
Figure 20. Implantable optogenetic device.
Figure 21.
Simultaneous large scale neural recording SiNAPS probe for
in vivo
neuroscience research.
You Na Lee et al. [
28
] proposed a pH image sensor for chronic recordingsand obtaining
the neurochemical signals that contain the spatiotemporal information. The image sensor
can detect the hydrogen ions’ distribution change while carrying the insertion test at the
brain, and then it reproduces the two-dimensional data from the measured data. The pixel
circuit is a two-transistor (2-T) in the pixel circuit, which is advantageous. It has a low pixel
pitch and high-density pixel array to enhance the visualization of spatial changes in the
distribution of pH. Its black-box approach is shown in Figure 22.
Figure 22. pH recording using CMOS image sensor.
Heymes et al. [
29
] developed an IMIC circuit, a MAPS prototype (Monolithic Active
Pixel Sensor), to incorporate the intracerebral positron probes, which are used for imaging
Sensors 2021,21, 2098 15 of 38
the deep brain of the active rats as shown in Figure 23. This novel equipment of built-in
probes using the CIS had tolerable overheating in the brain tissue of the rat or any other
small animal. The author and their team are also working to generate the next generation
of probes having reduced power dissipation with fewer pixels to be implemented in a
real-time test.
Figure 23.
(
a
) Schematic of the experimental setup. (
b
) Physical setup. Figure adapted with permis-
sion from [29].
Summary
It is mandatory to monitor the brain’s neural activities to detect the early stages
of brain-related disorders like epilepsy, Parkinson’s disease, Alzheimer’s, and tumors.
We studied the systems in terms of invasiveness, pixel size, and device size for neural
activity measurement. Among them, neural activities measurement [
24
] is efficient due
to its minimal invasiveness with a pixel size of 7.5
µ
m to image the neural activities. In
addition, two types of image sensors, namely planar and needle type, are introduced.
Intra brain image transmission [
25
] is efficient with its functional capabilities such as
wireless transmission and deep brain imaging with the same pixel size of 7.5
µ
m, whereas,
for simultaneous measurements, on-chip bioimaging [
23
], optogenetic device [
26
], and
SiNAPS [
27
] are used, in which optogenetic device [
26
] is preferred due to its low pixel
size and simultaneously performance of brain imaging and optical stimulation. Positron
imaging [
29
] provides 100% detection efficiency with wireless transmission, but it affects
physical activity due to its implanting facility.
3.3. Disease Diagnosis Systems Related to Skin
From the last decade, the research in biomedical healthcare instruments has been
rapidly increased across the world. The investments in developing compact, low-power ap-
plications that could hold an extensive health information range are in great demand. Dur-
ing their professional works, staff members and interventional radiologists are frequently
exposed to ionizing radiation in a low dose. These exposures can cause effects like aged
skin, hand depilation, and radiodermatitis or may cause skin cancer. Elia Conti et al. [
30
]
focused on one of these possible applications in Interventional Radiology (IR), a device
to perform online monitoring of all the people involving in interventional activities. This
portable device can measure the accumulated dose in real time and have an alarm to
reduce the possibility of dose and collect the offline dose measurement results to create a
temporal profile for the absorbed dose measurements. It gets correlated to staff-specific
activities during the research. The black-box approach of the dosimeter is shown in
Figure 24. This proposed system is a part of the Italian Real-time Active Pixel Dosimetry
(RAPID) project framework.
Sensors 2021,21, 2098 16 of 38
Figure 24. Active personal dosimeter in interventional radiology.
Summary
This system is portable and helps in real-time monitoring of the staff involved in
IR, thus helping the interventionists to estimate the radiation tolerance and plan further
interventional procedures. However, the dosimeter is inadequate in terms of technology to
meet all the types of interventional laboratory procedures.
3.4. Disease Diagnosis Systems Related to Intestines
The gastrointestinal tract screening started in the past few years and still is widely
used now with the help of an endoscope, which is sent into the human body through the
natural orifice. It is a painful process and needs anesthesia to be given to the patient while
doing it. However, the latest developments in wireless capsular endoscopy make this a
noninvasive process, and it has become a completely painless procedure. Covi et al. [
31
]
proposed a camera module system to be used in endoscopic applications like endoscopic
capsules. The prototype of the camera module is shown in Figure 25.
Figure 25.
(
a
) Camera module prototype. (
b
)
In vivo
experiments in pig’s stomach. (
c
) Acquired
image of recorded video. Figure adapted with permission from [31].
Experiments were made on a porcine model to evaluate the camera module perfor-
mance in terms of its illumination and image quality in real time. This camera module
is connected to the capsular shell, which is wired with a computer and sent into the pig
stomach to visualize the required regions of interest, and the video was captured about
gastroscopy for 30 min, which can be seen in Figure 25b,c. The quality of video of captured
images is good, sufficient for the physicians to make a suitable diagnosis. Due to its low
manufacturing, this module can be used for disposal applications.
The gastrointestinal tract’s noninvasive screening and diagnostic assessment can be
made using wireless capsule endoscopy (WCE). The first capsule endoscopy entered the
market in 2001. This capsule has size and shape of a pill, and it consists of a small, low-
resolution camera with LED illumination, a radio transmitter, and two batteries. This
capsule passes through the intestines and sends the images to the data recorder attached to
the patient.
The major activity of research in wireless capsule endoscopy focuses on the automatic
identification of regions in the gastrointestinal tract with unusual conditions like bleedings
Sensors 2021,21, 2098 17 of 38
and tumors. The incoming generation of this wireless capsule endoscopy features real-
time manipulation remotely to verify any damage in an organism’s tissue. Narrow-band
imaging enhances the imaging of the mucosal microvascular structure. To transmit the data
images under real-time manipulation of wireless capsule endoscopy, it requires efficient
image compression. Pawel Turcza et al. [
32
] developed an image processing system for
wireless capsule endoscopy in which a dedicated image coder is inserted. The black-box
approach of wire capsule endoscopy is shown in Figure 26.
Figure 26. Wireless capsule endoscopy.
Tong xi Wang et al. [
33
] designed an ultra-low power CIS particularly used for en-
domicroscopy applications. This image sensor has a dual operation mode, which leads to
self-powering internally. The pixel can also be converted into a solar energy cell to harvest
the energy for the sensor’s operation, and its black-box approach is shown in Figure 27.
Figure 27. Endomicroscopic application with self-energy harvest technology.
Summary
Among the three systems discussed/reviewed earlier disposable endoscopic applica-
tion [
31
] is having the advantages of providing highly efficient illumination, acceptable
image quality with a FOV (field of view) value of 60 degrees, and disposable. However, it
is still a painful procedure that requires the patient to be anesthetized. Wireless capsule
endoscopy [
32
] has image compression capability with low power consumption and wire-
less transmission, but this device does not work for long-duration surgeries and need an
external power supply. Endomicroscopic application [
33
] is also a low-power operating
device with on-chip image data analysis, but the sensitivity is poor in low light conditions.
3.5. Disease Diagnosis Systems Related to Eyes
From the last 10 years, different information technologies were developed, and the
importance of security systems is also increased for mobile phones. The level of security in
Sensors 2021,21, 2098 18 of 38
biometrics, involving voice, retina pattern, fingerprints, etc., is quite substantial since the
chance of making duplicates is significantly less. The development of Iris detection has been
rapidly increasing to unlock mobile phones like iPhone X. The Iris detection algorithm’s
traditional process involves image acquisition, Iris image enhancement, binarization, and
recognition. However, the output of this process is CIS that yields multi-bit data. So, Soo
Youn Kim et al. [
34
] proposed a single-bit CIS, which can show the Iris recognition process
shown in Figure 28.
Figure 28.
(
a
) Iris recognition process with proposed CIS. (
b
) CIS chip on printed circuit board
(PCB) [34].
Retinal implants make great efforts to restore patients suffering from retinal diseases
such as age-related macular degeneration and retina pigmentosa. Concerning anatomy,
a retinal prosthesis is classified as subretinal, epiretinal, suprachoroidal devices. Among
the classifications, the subretinal implant is providing a high pixel density of 1600 pixels.
Hosung Kang et al. [
35
] developed a stimulator with low mismatch ability integrated with
photodiode used for a subretinal prosthesis, as shown in Figure 29.
Figure 29. Subretinal prosthesis [35].
Subretinal Prosthesis Simulator called as “SPStim” demonstration is done using pig
eyeball, which was shown in Figure 30a, and its ex vivo setup can be seen in Figure 30b. This
“SPStim” is well suitable for subretinal implants, and it has very little power consumption.
Figure 30. (a) Pig eyeball. (b) Ex vivo experiment setup using a dissected pig eyeball [35].
Image sensors are acting as additional optical units that can capture scenes in the
latest retinal implantations. Hundreds of thousands of patients have started steadily
losing their vision or getting blind because of retinal degenerative diseases. Chaunqing
Zhou et al. [
36
] proposed an implantable imaging system for a visual prosthesis. A micro
camera is developed for this purpose and devised to fit in the rabbit’s lens capsule, and
Sensors 2021,21, 2098 19 of 38
biocompatible silicon is used to encapsulate it. For the clinical procedure, 12 micro cameras
having CIS into 12 eyes of 12 rabbits were implanted. Its black-box approach is shown
in Figure 31.
Figure 31. The implantable micro camera system in the visual prosthesis.
3.6. Disease Diagnosis Systems Related to Heart
Due to drastic development in integrated circuit manufacturing, wearable devices like
smart jackets and smart bands are used widely to assess human heart and motion detection
with the help of electrodes. Inertial motion sensors are integrated into them. However,
these smart devices are causing potential risk due to leakage in current and discomfort
using them for a long time. Due to this reason, people with sensitive skin or neonates are
suffering from rashes and allergies after wearing smart wearable devices for a longer time.
To overcome this problem, contactless sensors are being introduced. Yu-Chen Lin [
37
]
et al. developed a contactless pulse rate detection and motion status monitoring system.
The motion status is obtained by complexion tracking with a motion index developed to
eliminate motion artifacts to enhance pulse rate measurement accuracy. Near-infrared
LEDs are attached to measure in dark conditions. A switch is used to enable the dark
mode and brightness mode detections. An experimental trial is conducted on 10 volunteer
people, including three females and seven males between 22 and 30 years with no earlier
heart-related disorders. The people sit in front of the system with a distance of 50 cm
for pulse rate detection. The smartphone is connected to the developed system using
Bluetooth, and results are displayed on the Android application in the smartphone shown
in Figure 32.
3.7. Disease Diagnosis Systems Related to Lungs
Around 97 million people are affected, and over 2 million people are died due to
COVID-19 (coronavirus disease) worldwide. More significant outbreaks occurred in the
United States, Italy, Spain, India, Brazil, Russia, etc. The symptoms of this disease are
predicted accurately. The infected people may have a cough, fever, shortness of breath,
muscle aches, fatigue, and causes pneumonia attack that severely leads to acute respiratory
distress syndrome and causes death. Due to unfortunate outbreaks, hospitalizations are
increased in huge numbers, and the overwhelming need for intensive care units is necessary
to save the patients from dying. Michael P. McRae et al. [
38
] developed an integrated care
COVID-19 clinical decision supporting system to find the severity score of the COVID-19
patients to prioritize their care and resources for treating them in which a disposable
programmable bio nanochip (p-BNC) is used to determine the severity of COVID-19 as
shown in Figure 33.
Sensors 2021,21, 2098 20 of 38
Figure 32.
(
a
) System architecture. (
b
) Experimental setup. (
c
) Data display on Android application in the smartphone [
37
].
Figure 33.
(
a
) The cartridge used in programmable bio nanochip (p-BNC) assay system. (
b
) Portable
instrument. (
c
) SEM image, a fluorescent micrograph of bead sensors. Figure adapted with permission
from [38].
Most of the complications in COVID-19 are caused by a cytokine storm. It triggers the
immune system to include the inflammatory proteins known as cytokines, which kill tissues
and organs in a human body. Yujing Song et al. [
39
] developed a clinical application called
“PEdELISA (pre-equilibrium digital enzyme-linked immunosorbent assay) microarray”
to monitors the cytokines in the severely affected COVID-19 patients who are admitted
to intensive care units in a hospital. The developed system is having a fast four-plex
measurement of cytokines in the serum of COVID-19-affected people, and the schematic
diagram is shown in Figure 34.
Sensors 2021,21, 2098 21 of 38
Figure 34.
(
a
) Pre-equilibrium digital enzyme-linked immunosorbent assay (PEdELISA) system-
photo image. (
b
,
c
) PEdELISA system schematic in a bio-safety cabinet. Figure adapted with permis-
sion from [39].
Lack of risk assessment of COVID-19 airborne particle transmission in public envi-
ronments like classrooms, elevators, restaurants, and supermarkets leads to uncertainties
in their preventive measures to control the spread of COVID-19. Siyao Shao et al. [
40
]
conducted a risk assessment of exhaled particles from different people in various environ-
mental settings, which are becoming local hotspots for tremendous outbreaks of COVID-19.
Different breathing patterns are being recorded from 8 healthy participants between the
ages between 21 and 29 with a breathing rate of 76 beats per minute, nose inhales for two
beats, while mouth exhale for three beats. The breathing patterns are followed the Schlieren
imaging breathing technique for all the experiments.
The digital inline holography (DIH) technique is used to assess the individual mi-
croparticles in a sample volume. Moreover, 1X magnification digital inline holographic
imaging system is involved in capturing the exhaled particles in size from 10 to 50-
µ
m
range; 20
×
magnification digital inline holographic imaging system in capturing the ex-
haled particles, which are in size from 3 mm to 2-cm range, is shown in Figure 35. To
improve the COVID-19 disease control efforts, Bo Ning et al. [
41
] developed a compact
saliva-based COVID-19 assay using a smartphone without using the laboratory equipment
that provides the results in 15 min. This procedure is easier than RT-PCR test, which is
currently used to perform the COVID-19 test. The assay used in the proposed test uti-
lizes Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)Cas12a activity
triggers the virus DNA, and this CRISPR reads COVID-19 from the patient’s saliva and
nasal swabs since the RNA level in both places is similar. The smartphone fluorescence
microscope device is used to read the CRISPR fluorescence detection system assay to
analyze saliva from COVID-19-affected patients, shown in Figure 36.
Figure 35.
(
a
) The head position of the participant. (
b
) Breathing direction of the participant.
(
c
) 1
×
DIH measurement system. (
d
) 20
×
DIH measurement system. Figure adapted with permission
from [40].
Sensors 2021,21, 2098 22 of 38
Figure 36.
(
a
) The 3D-printed schematic of smartphone fluorescence reader (
b
) Saliva-based CRISPR
Fluorescence Detection System (FDS) Assay procedure. (
c
) Capture images of CRISPR-FDS Assay [
41
].
Summary
Among the four systems reviewed, COVID-19 severity detection [
38
] uses the statisti-
cal learning algorithm and provides the severity score result in 16 min to give the suitable
treatment and predict mortality with accuracy. Still, some of the input biomarkers yield
redundant data that cause different results. COVID-19 cytokine storm monitoring [
39
]
maintains high speed and sensitivity compared to existing in analog label-free point-of-care
systems, but the system’s capacity is limited to 16 samples. COVID-19 risk assessment [
41
]
provides the possibility of spreading of COVID-19 droplets with good accuracy. It proved
that multiple ventilation could reduce the spreading. COVID-19 saliva test [
40
] is having
a good correlation, portable. It provides the results in 15 min with 98.7% accuracy and
is user friendly as no medical experience is needed, but the error occurs due to lysing of
saliva for analysis.
3.8. Disease Diagnosis Systems Related to Bones
Nowadays, people are getting old, and at their above-average age, people are getting
through arthroplasty surgery for their damaged hip joints to be replaced. Various visual
aided systems were introduced in order to reduce the risk factor earlier. Syed Mudassir
Hussain et al. [
42
] proposed an image sensor that can replace a bulky camera from the
pelvis’s femoral head, which saves most of the system’s power. A new technique was
introduced to identify every pattern from the situation covered with blood, which generates
the respective binary ID. The vision-based smart trail with test setup is also represented,
and its black-box is shown in Figure 37.
Figure 37. Pose estimation platform for total hip arthroplasty.
Sensors 2021,21, 2098 23 of 38
Nowadays, the population which is suffering from arthritis is growing very rapidly.
By 2030, in the United States, the affected people above the age 65 and older would cross
41 million. Among those people, knee arthritis affects the quality of life in a depreciation
manner. The surgical treatment for this device is total knee arthroplasty (TKA), in which
an artificial implant will replace the affected knee joint. Shaolin Xiang et al. [
43
] proposed a
wireless acquisition system that can be used in TKA surgeries. This system will directly
provide the inside knee implant images to the workstation wirelessly using a wireless data
logger. Its black-box is shown in Figure 38.
Figure 38. Wireless image acquisition system for knee replacement implant in total knee arthroplasty.
Summary
The two systems, pose estimation platform for total hip arthroplasty [
42
] and knee
implants [
43
], are playing a major role in assisting doctors by providing real-time images
during surgeries for better accuracy and quality. In addition, using these devices reduce the
chances of failures and side effects in surgeries. These two devices can work in a low-light
environment also.
3.9. Disease Diagnosis Systems Related to Bacteria Cells
Biofilms are nothing but a pack of bacterial cells with extracellular polymer substances
automatically generated by bacteria by exposing them to rough atmospheric conditions.
These biofilms are majorly present on surfaces of pipes, dead tissues, and medical instru-
ments used for
in vivo
experiments. The effects of biofilms include medical device damage
and degradation of the drinking water quality. It is difficult to remove these biofilms from
water, medical instruments, and pipes.
This biofilm’s major effect is that it causes infection by Pseudomonas aeruginosa and
makes pneumonia untreatable to the patients with less immunity, which can lead to
death. Yeon Hwa Kwak et al. [
44
] proposed a lens-free CIS, which is low cost and easily
identifies colorimetric changes in biofilms concentrations. This lens-free CIS-based biofilm
detection platform is a simple and less costly method with a wide range of possibilities
to be used in medical and environmental biofilm detections, shown in Figure 39. Guoan
Zheng et al. [
45
] developed an on-chip cell imaging platform based on SPSM (subpixel
perspective sweeping microscopy). The prototype of ePetri is kept in a square plastic box
with a glue and polydimethylsiloxane (PDMS) layer that is used to cover the prototype,
and its black-box approach is shown in Figure 40. This layer is thin and prevents cultural
evaporation during the exchange of carbon dioxide between the plastic box and cover.
Sensors 2021,21, 2098 24 of 38
Figure 39.
(
a
) RGB (Red Green Blue) LED illuminations. (
b
) Lens free CMOS image sensor-based
biofilm detection. (
c
) The pattern of biofilm. Figure adapted with permission from [
44
], Elsevier, 2014.
Figure 40. ePetri dish.
The smartphone screen is used as a light scanner. It is a lensless microscopy imag-
ing system that can capture confluent cell structures through random light resources. It
can also give high-resolution imaging and can be used as an emerging tool for
in vitro
cell observations. Nowadays, flat-panel images are the common detectors in biomedical
applications in which large imaging areas with high frame rates are required. Fluoroscopy,
mammography, proteomics, and image-guided radiotherapy need large imaging areas
having real-time frame rates. However, flat-panel imagers are having drawbacks such as
large pixels, low frame rates, and high noise. To overcome these drawbacks, active pixel
sensors came into the picture and received good fame by having features like low noise,
high frame rate, and high pixel resolution, low power consumption, and low manufac-
turing cost. Esposito et al. [
46
] developed a new radiation-hard monolithic active pixel
sensor called (Dynamic range Adjustable for Medical Imaging Technology) DynAMITe.
This imager consists of two separate resolutions with different saturation performances and
different noise in the same pixel array. This design had tremendous advantages in various
biomedical applications, which need a high dynamic range, high frame rate, and large
pixel area. Lensless imaging systems play a crucial role in biomedical imaging applications
like cancer cell detection and medical tomography, Optofluidic microscopy, etc. Previously,
Sensors 2021,21, 2098 25 of 38
microfluidic CIS has a limitation of low sensitivity. However, pixel size should be consid-
ered to improve the sensitivity, but it reduces the resolution. Wang et al. [
47
] introduced a
super-resolution CIS for biomicrofluidic imaging applications. The authors conducted an
on-chip single frame super-resolution image processing algorithm also. Polystyrene bead
is a colloid particle used to simulate the cell’s behavior in a biomicrofluidic system, and its
black-box approach is shown in Figure 41.
Figure 41.
Cancer cell separation by gravity from the normal cell through biomicrofluidic imaging
device.
Junhe lee et al. [
48
] proposed a portable and affordable lens-free CIS-based bacterial
cell detection platform, which can measure colorimetric light signals enzyme-linked im-
munosorbent assay process ELISA Process. This process had become the famous process
to detect the molecules and thereby analyze the change in the colorimetric degree of a
molecule in immune reactions in almost all biomedical research. This ELISA detection
platform uses multimillion pixel arrays, which are CIS instead of single photodiodes shown
in Figure 42.
Figure 42.
(
a
) CIS-based ELIZA detector. (
b
) Absorbance measurement and ELIZA assay. Figure
adapted with permission from [48], Elsevier, 2014.
This detection system had a great role in prominent applications like blood analysis,
environmental monitoring, and pathogen detection. In medicine and life sciences, the
fundamental methods are fluorescence-based time-resolved analysis techniques. In those
methods, fluorescence lifetime imaging microscopy (FLIM) is a promising measurement
process in biomedical applications. Min Woong Seo et al. [
49
] proposed the sub-nanosecond
time-gated four tap lock-in pixel CMOS image sensor with an in-pixel pulse generator (PG)
to be used in fluorescence lifetime imaging system. The i black-box approach is shown
in Figure 43.
Sensors 2021,21, 2098 26 of 38
Figure 43.
Fluorescence lifetime imaging microscopy is used to capture color fluorescence lifetime images of HeLa cells
with DAPI nucleus.
Ruopei Feng et al. [
50
] proposed a pipeline that joins the meganuclease-mediated
transformation with fluorescence detected temperature jump microscopy to capture fast
dynamics of biomolecules in living multicellular organisms with single-cell resolution. The
demonstration is also done on folding kinetics and stability of the fluorescence resonance
energy transfer-labeled glycolytic enzyme phosphoglycerate kinase in individual cells
of four zebrafish tissues. CMOS camera (Figure 44) is used to record the millisecond
time resolution moves of kinetics in the living zebrafish cell, i.e., keratinocytes, eyelids,
notochord, and myocytes.
Figure 44. Temperature jump fluorescence Imaging microscope [50].
Another push of smartphone technology into personal healthcare systems is using
tissue and cell imaging applications to identify the accurate disease and health information.
Tsung-Feng Wu et al. [
51
] proposed a unique approach to develop a good-quality
cytometer compatible with smartphones, shown in Figure 45. For the demonstration,
HEK293 cells and isolated white blood cell samples, inserted between the two glass cover
strips, are taken to analyze the cytometer. The CIS captured the images with a frame
rate of 10 fps. It is a label-free cell analyzer that is able to detect the internal structure of
cells, and it is well suitable for point-of-care diagnostics. Aldo Roda et al. [
52
] presented
a smartphone-based biochemiluminescence detection, a portable chemistry platform for
point-of-care analysis. This system can capture and measure the biochemiluminescence
attached with biospecific enzymatic reactions in biological fluids like oral fluid and blood.
Its black-box approach is shown in Figure 46.
Sensors 2021,21, 2098 27 of 38
Figure 45.
(
a
) Scattering image-based cytometer (
b
) Images of traveling bead over the sensing area in
different time intervals from dark field to bright field. Figure adapted with permission from [
51
],
Royal Society of Chemistry, 2014.
Figure 46. Biochemilunimescence detection.
The 3D printer is used to design and fabricate the prototype accessory to be attached
to the smartphone. This prototype had dual functions to act as a dark box to cover from
ambient light and carries the mini cartridge used for diagnostic purposes and chemical
fusions. This is an excellent instrument that could be used in fast liver function evaluations
in dogs and cats.
Summary
From the reviewed data, Biofilm detection [
44
] is cost-effective and using lens-free
CMOS imaging technology with a large field of view 25 times larger than the general
optical microscope. ePetri dish [
45
] captures the cell growth of contagious cell cultures. It
is available at an affordable price, which is disposable and recyclable, but the system is
very slow and limited to non-fluorescence imaging. DynAMITe [
46
] has two-side buttable
technology with two different pixel sizes, which yields more pixel size imager in biomedical
imaging. It offers high frame rates and high spatial resolution. Biomicrofluidic imaging [
47
]
can separate the cancer cells from normal cells and adapt to pixel size from high to low with
high frame rates. Still, it needs to improve the low light sensitivity, which is affecting the
resolution. ELISA detector [
48
] is noise-free and low cost with compact in size. The pixel
size is significantly less sufficient to image the bacteria cell detection, but a discontinuity
in light transmission will occur at some concentrations due to biological procedures and
Sensors 2021,21, 2098 28 of 38
dilution errors. FLIM [
49
] is having fast data acquisition with low noise and error, but
it comprises higher voltages and complex circuits. Quantifying protein dynamics [
50
]
is available to perform both
in vivo
and
in vitro
tissue type experiments. The proposed
pipeline methodology helps in photodynamic cancer therapy, but this system cannot obtain
individual cells’ time-resolved data within live vertebrate organisms. Intracellular imaging
and biosensing [
51
] is the first device to provide significant information about cell features
like size, shape, and life cycle. Still, the magnitude intensity difference is beyond the
dynamic range. Biochemiluminescence detection [
52
] performs frequent and noninvasive
bile acid monitoring with a coefficient of variation from 5% to 12% within a time period of
2 days.
4. Data Extraction and Evaluation
The extracted data from the conducted literature study meets our criteria for the
review. The data consists of Table A1 (Appendix A) containing the information from our
study (e.g., title, year of the article, CIS model/camera module; type of fields included
e.g., medical, biomedical, and implantable), application name/target, and CIS technical
specifications included (e.g., pixel size, resolution, pixel pitch, sensitivity, dynamic range,
signal to noise ratio, fill factor, area, and power and frame rate). The data for all studies
are extracted independently by two authors S.B.S. (Suparshya Babu Sukhavasi) and S.B.S.
(Susrutha Babu Sukhavasi), under the supervision of author K.E. and discussion with other
authors (A.E. and S.A.).
In this section, the basic evaluation of each device’s vital parameters is given as per
our survey. We took five parameters as described in Table 1. We have done a quantitative
evaluation for the articles reviewed based on the key parameters in terms of their medical
improvement for the diagnosis of various diseases.
Table 1. Parameters considered for system evaluation.
Description
Disease diagnosis Default value 10.0 is assigned to every system that helps diagnose diseases like anemia, arthritis, brain
disorders, etc., related to the human body.
Testing method Every system needs to follow one of the testing methods such as in vivo, in vitro, or both to perform
disease diagnosis.
Remote sensing Remote sensing of a system allows doctors to monitor patients’ health status by accessing information
timely about their health status or their vital signs without the need for physical presence.
Analysis type
The system that is physically implemented or embedded in an application involved in disease diagnosis
is considered real time. The system that involves the only simulation cannot be considered real time.
Pain level
The pain scale helps doctors decide accurate diagnosis and treatment plans to select medical devices for
disease diagnosis.
The device assisting the medical diagnosis requires a lot of parameters, among them,
the parameters like the testing method that involves in vivo or in vitro or both the testing
methods, works real time or not, remote sensing possibility for ease of diagnosis helps
in disease diagnosis in various organs of the human body and pain scale for patients’
tolerance check; these parameters are playing a crucial role in data evaluation.
The parameters that we used for evaluation are to be considered as basic fundamental
parameters for medical device selection in diagnosing the human body. Uniform weightage
of 10.0 is assigned to all parameters initially and varies concerning their available options.
For example, we gave the weightage of 10.0 for the glucose-sensing system because the
system can perform testing in both
in vivo
and
in vitro
methods, where an 8.0 value will be
assigned for the system doing
in vivo
testing method, and a 6.0 value will be assigned for
the system doing
in vitro
testing method. The value 10.0 is assigned for a system having
remote sensing ability, and value 5.0 is assigned for systems with no remote sensing. The
value 10.0 is assigned for the system that can be implemented in real time and value 5.0 is
Sensors 2021,21, 2098 29 of 38
assigned for the system with no real-time implementation. We used (numerical rating
scale) NRS pain scale [
53
] for pain assessment and to rate the pain level of the patients in
between 0 and 10 numerical values such that the value 0 refers to none, the value 1–3 refers
to mild pain, the value 4–6 refers to moderate pain, and the value 7–10 refers to severe pain.
Every single parameter has its own impact on the diagnosis procedure, and the score is
given parameter-wise based on the available data in the literature survey.
Using the below normalization formula (Equation (1)), we have calculated the total
score for every system based on Table 2. The total score of every system in Table 2provides
a brief evaluation of the medical devices’ abilities.
Total Score =
N
k=0
10Pk
N+2, (1)
where kis the particular parameter (e.g., pain level) used in the evaluation, Nis the total
number of parameters of each system, P
k
represents the parameter value of each system
,
and the value 2.0 is added as a constant to the equation to help in highlighting the system’s
evaluation from its parameter score.
Figure 47 shows a clear pictorial representation of each system with a total score to
every system involved in our literature survey. The systems with a higher score of more
than 90% indicate the medical system is advanced, can be accessed remotely, implemented
in real time with good efficacy, and has a reasonable pain scale to perform disease diagnosis.
IRIS application obtained a very less score of 32% with a zero pain scale due to neither
involving in any diagnosis procedures nor performing any testing methods than other
systems. The average score obtained by most of the systems is in the range between
74% and 78%. Intra brain image transmission, positron imaging, total hip arthroplasty,
knee implant systems scored 96% as they had remote sensing capabilities and real-time
implantation and involved testing methods to perform disease diagnosis.
Figure 47. System evaluation represents the total score of every medical system.
Sensors 2021,21, 2098 30 of 38
Table 2. Parameter-based system evaluation.
System Related To Helps in Disease
Diagnosis Score Testing
Method Score Remote
Sensing Score Real Time or
Not Real Time Score Pain Level Pain
Scale
Total
Score
Hemoglobin measurement [11] Blood Anemia 10 In vitro 6 No 5 Yes 10 Severe 7 78
Oxygen detection in blood [12] Blood Diabetes mellitus 10 In vitro 6 No 5 Yes 10 Moderate 6 76
Microfluidic cytometer [13] Blood Cardiovascular diseases 10 In vitro 6 No 5 Yes 10 Moderate 6 76
Finger powered agglutination lab chip [14] Blood Bacterial infection 10 In vitro 6 No 5 Yes 10 Moderate 5 74
Blood flow velocity detection [15] Blood Peripheral artery disease 10 In vivo 8 No 5 Yes 10 Severe 9 86
Glucose sensing [16] Blood Diabetes mellitus 10 both 10 Yes 10 Yes 10 Moderate 5 92
HIV diagnosis [17] Blood HIV 10 In vitro 6 Yes 10 Yes 10 Severe 7 88
Whole blood glucose analysis [18] Blood Diabetes mellitus 10 In vitro 6 No 5 Yes 10 Severe 7 78
Water salinity detection [19] Blood Diabetes mellitus 10 In vitro 6 Yes 10 Yes 10 None 0 74
Real-time toxic gas detection [20] Blood Leukemia 10 In vitro 6 Yes 10 Yes 10 Moderate 5 84
Portable chemical-free hemoglobin assay [21] Blood Anemia 10 In vitro 6 Yes 10 Yes 10 Moderate 6 86
Blood analysis [22] Blood Anemia 10 In vitro 6 Yes 10 Yes 10 Moderate 6 86
On-chip bioimaging [23] Brain Brain disorders 10 both 10 No 5 Yes 10 Severe 9 90
Neural activities measurement [24] Brain Brain disorders 10 In vivo 8 No 5 Yes 10 Severe 8 84
Intra brain image transmission [25] Brain Brain disorders 10 In vivo 8 Yes 10 Yes 10 Severe 9 96
Optogenetic device [26] Brain Brain disorders 10 In vivo 8 No 5 Yes 10 Severe 10 88
SiNAPS [27] Brain Brain disorders 10 In vivo 8 No 5 Yes 10 Severe 9 86
pH recording [28] Brain Brain disorders 10 In vitro 6 No 5 Yes 10 Severe 7 78
Positron imaging [29] Brain Brain disorders 10 In vivo 8 Yes 10 Yes 10 Severe 9 96
Active personal dosimeter [30] Skin Skin cancer 10 N/A 0 Yes 10 Yes 10 Mild 1 64
Disposable endoscopic application [31] Intestines GI tract diseases 10 In vivo 8 No 5 Yes 10 Severe 8 84
Wireless capsule endoscopy [32] Intestines GI tract diseases 10 In vivo 8 Yes 10 Yes 10 Severe 8 94
Endomicroscopic application [33] Intestines GI tract diseases 10 In vivo 8 No 5 No 5 Severe 8 74
IRIS application [34] Eyes N/A 0 N/A 0 Yes 10 No 5 None 0 32
Subretinal Implanted chip [35] Eyes Retinal diseases 10 Ex vivo 6 No 5 Yes 10 Severe 10 84
Visual Prosthesis [36] Eyes Retinal diseases 10 In vivo 8 No 5 Yes 10 Severe 10 88
Contactless pulse rate detection [37] Heart Shortness of breath 10 N/A 0 Yes 10 Yes 10 None 0 62
COVID-19 severity detection [38] Lungs COVID-19 10 In vitro 6 No 5 Yes 10 Severe 7 78
COVID-19 Cytokine storm monitoring [39] Lungs COVID-19 10 In vitro 6 No 5 Yes 10 Severe 7 78
COVID-19 risk assessment [40] Lungs COVID-19 10 In vitro 6 No 5 Yes 10 None 0 64
COVID-19 Saliva test [41] Lungs COVID-19 10 In vitro 6 No 5 Yes 10 None 0 64
Pose estimation platform for total hip arthroplasty [42] Bones Arthritis 10 In vivo 8 Yes 10 Yes 10 Severe 9 96
Knee Implants [43] Bones Arthritis 10 In vivo 8 Yes 10 Yes 10 Severe 9 96
Biofilm detection [44] Bacteria Cells GI tract diseases 10 In vivo 8 No 5 Yes 10 Severe 8 84
ePetri dish [45] Bacteria Cells Tissue damage 10 In vitro 6 Yes 10 Yes 10 Severe 7 88
DynAMITE [46] Bacteria Cells Breast cancer 10 N/A 0 No 5 Yes 10 Severe 9 70
Biomicrofluidic imaging [47] Bacteria Cells Cancer 10 In vitro 6 No 5 Yes 10 Severe 8 80
ELISA detector [48] Bacteria Cells Listeriosis 10 In vitro 6 No 5 Yes 10 Severe 7 78
FLIM [49] Bacteria Cells Hepatitis 10 In vitro 6 No 5 Yes 10 Severe 7 78
Quantifying protein dynamics [50] Bacteria Cells Cancer 10 both 10 No 5 Yes 10 Severe 9 90
Intracellular imaging and biosensing [51] Bacteria Cells Cancer 10 In vitro 6 No 5 Yes 10 Severe 7 78
Biochemiluminescence detection [52] Bacteria Cells Cholestatic liver disease 10 In vitro 6 No 5 Yes 10 Severe 7 78
Sensors 2021,21, 2098 31 of 38
5. Discussion
After reviewing the blood-related disease diagnosis systems, we found the most potent
disease diagnosis systems related to blood is portable chemical-free hemoglobin assay [
21
]
due to its least blood sample size of less than 1
µ
L, giving result in less than 8 s with an
accuracy of 99% and having advantages of its low cost, chemical-free procedure, and no
medical personnel is needed. Blood analysis [
22
] can provide the density of RBC (red blood
cells) and WBC (white blood cells), along with hemoglobin measurement with an accuracy
of 93% and provides the result in 10 s by taking the blood sample of 10
µ
L. The most
potent disease diagnosis system related to the brain is intra brain image transmission [
25
],
which covers the brain’s surface and imaging the cross-section with very low invasiveness
compared to other systems and portable size of less than 1 mm in volume. The potent
system related to intestines is endomicroscopic application [
33
] due to its energy-harvesting
capability inside of an imaging pixel. It operates in two modes: self-powering modes and
imaging pixel mode, which causes the system to consume ultra-low-power of 6 microwatts.
As per our study, we consider the potent system related to lungs is COVID-19 severity
detection [
38
] because, through this system, the disease severity of the patient can be
detected and thereby predict the mortality, from that, the decision can be made whether
the patient can be quarantined or needs to be treated in ICUs (intensive care unit). As per
the survey, the most powerful system related to bacterial cells is biofilm detection [
44
] that
uses lensFree imaging technology with an ultra-large field of view and zooming capacity
of 25 times larger than 100×optical microscope with three times more stability.
From the surveyed parametric data of Appendix A, we observed that the pixel pitch
plays a crucial role in blood-related disease diagnosis. The pixel pitch of 7.5
µ
m is enough
to perform blood analysis. In contrast, the pixel pitch range of 2.5–4
µ
m is required to
identify and separate red blood cells and white blood cells from the blood sample. For
brain-related disease diagnosis, while designing the device, the LEDs used to stimulate
causing the power dissipation are to be controlled in case of brain-related disease diagnosis.
This causes heavy stains in blood, which interrupts the imaging of the brain for neural
measurement activities. The array resolution should be high so that more details will be
captured related to blood vessels and flow of blood in the brain. The device’s size should
be miniaturized so that it will not add more weight to the brain for diagnosis. An imager’s
area to meet the design criteria for implant measurement diagnosis is to be in needle type,
and planar type design with a very slight thickness width range of 0.2–0.5 mm can easily
penetrate the brain hippocampus and cause fewer stains inside for brain-related diagnosis.
The pixel pitch of 2.2
µ
m is well suited for compact devices for cell screening, and imagers
with high resolution can be used to compare with a 100
×
microscope, which is efficient to
observe more minute details about the cell growth and lifecycle evaluations. In addition,
we observed that some of the papers were provided with a few parameters of CMOS image
sensors involved in the disease diagnosis systems. Still, the system capabilities are clearly
explained, which helped us in evaluating the systems.
CMOS Image Sensor Models
From the data collected in our literature survey, we have mapped the CMOS image
sensors models, camera modules embedded in medical devices performing disease diagno-
sis for the human body’s vital signs, and organs in Table 3. CIS manufactured with 0.18 and
0.35
µ
m technologies [
54
] were being used in most disease diagnosis systems. Samsung
smartphone was used in most smartphone-based disease diagnosis systems. CMOS image
sensors year-wise usage in disease diagnosis systems according to survey data is shown
in Figure 48.
Sensors 2021,21, 2098 32 of 38
Table 3.
The utilization of complementary metal-oxide semiconductor image sensor CIS models in different medical devices
performs disease diagnosis of vital signs, organs of the human body.
CMOS Technology/Image Sensor
Model/Camera Module Bacteria Cells Blood Bones Brain Eyes Heart Intestines Lungs Skin Grand Total
65 nm BSI CMOS 1 1
0.11 µm1 1
0.15 µm1 1
0.18 µm2 1 1 2 1 1 8
0.35 µm2 4 1 7
Apple iPhone Smartphone 1 1
Apple iPhone 5s Smartphone 1 1
Grasshopper 3 camera with Sony IMX174 1 1
LT225 1 1
MT9P031 3 1 4
Not mentioned 1 2 1 4
NAC Memrecam HX-5 1 1
NOON010PC30L 2 2
OV7680 1 1
OV8833 1 1
Samsung Galaxy S8 Smartphone 1 1
Samsung Galaxy SII Smartphone 1 1
Samsung S4 Smartphone 1 1
Samsung Galaxy S9 Smartphone 1 1
Sony Xperia E3 Smartphone 1 1
SONY α6100 1 1
TRDB_D5M 1 1
Grand Total 9 12 2 7 3 1 3 4 1 42
Figure 48.
CMOS image sensors year-wise usage in disease diagnosis according to survey data, where the x-axis represents
years and the y-axis represents the number of CMOS image sensors involved in disease diagnosis of human body organs.
6. Conclusions
This paper presented a systematic review and parameter-based evaluation of CMOS
image sensors incorporated disease diagnosis systems from the last 12 years. We reviewed
the systems and identified the advantages and disadvantages of every system involved
in the survey. Every system is evaluated by considering the parameters related to its
capabilities, implementations, and testing methods involved. The most potent systems with
prominent capabilities and functions are discussed, and organ wise involved CMOS image
sensor models are mapped. Despite CMOS image sensors’ existence in imaging technology
for the past two decades, this is sophisticatedly evolving into diversified demanding fields
with the immense collaboration of artificial intelligence, human psychology, chemical
compounds, etc., with the medical domain infrastructure.
Sensors 2021,21, 2098 33 of 38
Author Contributions:
This work has been primarily conducted by S.B.S. (Suparshya Babu Sukhavasi)
and S.B.S. (Susrutha Babu Sukhavasi), under the supervision of K.E., S.B.S. (Suparshya Babu
Sukhavasi), and S.B.S. (Susrutha Babu Sukhavasi) wrote the manuscript. Extensive discussions
about algorithms and techniques presented in this paper took place among the authors S.B.S. (Su-
parshya Babu Sukhavasi), S.B.S. (Susrutha Babu Sukhavasi), K.E., S.A. and A.E. over the past year.
All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not Applicable.
Informed Consent Statement: Not Applicable.
Data Availability Statement:
The data used in this review are from published primary studies,
which are available in the public domain.
Acknowledgments:
The authors acknowledge the University of Bridgeport for providing the neces-
sary resources to carry this research conducted under the supervision of Khaled Elleithy.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
CIS CMOS Image Sensor
CMOS Complementary Metal Oxide Semiconductor
CCD Charge Coupled Device
WCE Wireless Capsule Endoscopy
FPS Frames Per Second
dB Decibel
mm Millimeter
µm Micrometer
v/lux.s Volts per luminance. Second
DR Dynamic Range
SNR Signal to Noise Ratio
BSI Backside-illuminated
Sensors 2021,21, 2098 34 of 38
Appendix A
Table A1. Parametric data of complementary metal-oxide semiconductor (CMOS) image sensors used in medical devices performing disease diagnosis.
S.
No Year
CMOS
Technology/
Camera
Module
Pixel Size
(µm) Resolution
Pixel
Pitch
(µm)
Area Power
(W/mW)
SNR
(dB)
Sensitivity
(V/lux-s)
Frame Rate
(fps)
Dynamic
Range
(dB)
Fill Factor
(%) Field Application Name/Target
1 2014 0.35 µm 7.5 ×7.5 120 ×268 7.5 1048.6 µm×2700 µm N/A N/A N/A 58 Hz N/A 44% Implantable Blood Flow Velocity
Detection [15]
2 2014 0.35 µm 7.5 ×7.5 30 ×60 7.5 320 µm×790 µm N/A N/A N/A 10 fps N/A 31% Implantable Glucose Sensors [16]
3 2011 MT9P031 2.2 ×2.2 2592 H ×1944 V 2.2 5.70 mm ×4.28 mm 381 mW 38.1 db 1.4 14 fps 70.1 db N/A Medical Hemoglobin concentration
measurement [11]
4 2011 0.18 µm 27 ×33 32 ×32 N/A 1.9 mm ×1.5 mm 625 µw N/A N/A N/A N/A 56% Medical
Detection of luminescence
response from a xerogel
sensor array for O2
detection [12]
5 2017 65 nm BSI
CMOS 1.1 ×1.1 1600 ×2056 1.1 1.69 mm ×2.24 mm 182.8 mW N/A 1.05 45 fps N/A N/A Medical Microfluidic cytometer for
complete blood count [13]
6 2019 OV8833 1.4 ×1.4 3264 ×2448 1.4 4.6 mm ×3.45 mm 291 mW N/A 0.824 24 fps 67 dB N/A Medical Finger powered
agglutination lab chip [14]
7 2012 0.35 µm 15 ×7.5 128 ×268 N/A 2236 µm×3171 µm N/A N/A N/A N/A N/A N/A Biomedical On-chip Bio Imaging
sensor [23]
8 2012 0.35 µm 7.5 ×7.5 30 ×90 (needle),
120 ×268 (planar) 7.5
320 µm×1025 µm
(needle), 1000 µm×
3500 µm (planar)
N/A N/A N/A N/A N/A N/A Implantable Monitoring Neural
Activities [24]
9 2013 0.35 µm 7.5 ×7.5 60 ×60 7.5 1.0 mm ×1.0 mm N/A N/A N/A N/A N/A 30% Implantable
Wireless Imager for Intra
Brain Image
Transmission [25]
10 2017 0.35 µm 7.5 ×7.5 260 ×244 7.5 2200 ×2500 N/A N/A N/A 20 to 70 hz N/A N/A Implantable Optogenetic Device [26]
11 2018 0.18 µm N/A 512 ×512 28 330 µm×120 µm N/A N/A N/A N/A N/A N/A Implantable SiNAPS for Large Scale
Neuro Recordings [27]
12 2019 0.15 µm 2.2 ×2.2 256 ×256 2 10.42 mm ×3.55 mm N/A N/A N/A 3 0 fps N/A N/A Implantable Spatiotemporal pH
Recording [28]
13 2018 0.18 µm 30 ×50 16 ×128 18 480 ×6400 µm 115 µW N/A N/A N/A N/A N/A Implantable Positron Imaging in Rat
Brain [29]
14 2013 N/A 5.6 ×5.6 640 ×480 5.6 11.43 mm ×11.43 mm N/A N/A N/A N/A N/A N/A Medical Active Personal
Dosimeter [30]
15 2013 N/A N/A 320 ×240 N/A N/A 40 mw 53 dB N/A 24 fps N/A 25% Biomedical Wireless Capsule
Endoscopy [32]
16 2012 0.18 µm N/A 96 ×96 23 3 mm ×4 mm 6 µW N/A N/A 5 fps N/A N/A Biomedical Endomicroscope
Applications [33]
Sensors 2021,21, 2098 35 of 38
Table A1. Cont.
S.
No Year
CMOS
Technology/
Camera
Module
Pixel Size
(µm) Resolution
Pixel
Pitch
(µm)
Area Power
(W/mW)
SNR
(dB)
Sensitivity
(V/lux-s)
Frame Rate
(fps)
Dynamic
Range
(dB)
Fill Factor
(%) Field Application Name/Target
17 2009 N/A 2.2 ×2.2 648 ×488 2.2 1.43 mm ×1.07 mm 80 mw >36.5
db 1.1 30 fps 64 db N/A Medical Disposable Endoscopic
Applications [31]
18 2019 0.35 µm 114 ×117 64 ×64 N/A 4.3 mm ×3.2 mm N/A N/A N/A N/A N/A N/A Biomedical Stimulator for a sub
retinal prosthesis [35]
19 2010 OV7680 2.2 ×2.2 640 ×480 2.2 1443.2 µm×1082.4 µm 20 mW N/A 0.56 30 fps N/A N/A Implantable Visual Prosthesis [36]
20 2018 0.18 µm2.84 mm ×
2.84 mm 174 ×144 2.84 1.72 mm ×1.65 mm 12.36 mw N/A N/A 520 fps N/A N/A Medical Iris detection for biometric
applications [34]
21 2020
Grasshopper 3
camera with
Sony IMX174
5.86 ×5.86 1920 ×1200 5.86 11.43 mm ×11.43 mm N/A N/A 10.45 48 fps 67.55 dB N/A Medical COVID-19 severity
detection [38]
22 2020 SONY α6100 N/A 6000 ×4000 N/A 23.5 mm ×15.6 mm N/A N/A N/A 120 fps N/A N/A Medical COVID-19 Cytokine storm
monitoring [39]
23 2020
NAC
Memrecam
HX-5
N/A 2560 ×1920 N/A N/A N/A N/A N/A 35 fps N/A N/A Medical COVID-19 risk
assessment [40]
24 2021 Smartphone N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A Medical COVID-19 Saliva test [41]
25 2016 N/A N/A 240 ×240 N/A N/A N/A N/A N/A 8 fps N/A N/A Implantable Artificial Knee Implant
Surgeries [43]
26 2019 0.18 µm,
OV7660 15 ×15 200 ×200 15 3.5 mm ×3.5 mm N/A N/A N/A N/A N/A 60% Medical Total Hip Arthroplasty
Surgery [42]
27 2011 MT9P031 2.2 ×2.2 2592 ×1944 2.2 5.70 mm ×4.28 mm N/A 38.1 1.4 14 fps 70.1 db N/A Biomedical ePetri Dish [45]
28 2011 0.18 µm 50 ×50 2520 ×2560 50 12.8 cm ×12.8 cm N/A N/A N/A 30 fps 65 N/A Biomedical
DynAMITe (Dynamic
range Adjustable for
Medical Imaging
Technology) for
Bio-Medical Imaging [46]
29 2012 0.18 µm 10 ×10 128 ×128 10 2.5 mm ×5.0 mm N/A N/A N/A 1750 fps N/A N/A Biomedical
Bio-micro fluidic imaging
system for cancer cell
detection [47]
30 2014 MT9P031 2.2 ×2.2 2592 ×1944 2.2 5.70 mm ×4.28 mm 381 mw 38.1 db 1.4 14 fps 70.1 db N/A Biomedical ELISA detector [48]
31 2017 0.11 µm 22.4 ×22.4 128 ×128 22.4 7.0 mm ×9.3 mm N/A N/A N/A 45 fps N/A Biomedical
Real-Time Fluorescence
Lifetime Imaging
Microscopy [49]
32 2019 LT225 5.5 ×5.5 2048 ×1088 5.5 43 mm ×43 mm N/A N/A N/A 170 fps 56.4 N/A Biomedical Quantifying Protein
Dynamics [50]
33 2014 MT9P031 2.2 ×2.2 2592 ×1944 2.2 5.702 mm ×4.277 mm 381 mW 38.1 db 1.4 30 fps 70.1 N/A Medical Bio Film Detection [44]
34 2017 TRDB_D5M 2.2 ×2.2 2592 ×1944 2.2 5.702 mm ×4.277 mm N/A 38.1 db N/A 70 fps 70.1 db N/A Medical Noncontact Heart rate
detection [37]
Sensors 2021,21, 2098 36 of 38
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